Untitled

library(tidyverse)

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library(tidytext)
library(tm)

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library(SnowballC)
library(Rtsne)
library(ggplot2)
library(plotly)

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library(scales)

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library(wordcloud)

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library(viridis)

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library(DT)
library(plotly)
# Load and preprocess data
data = read_csv("https://raw.githubusercontent.com/JIHONGKING/Data_Analysis/refs/heads/main/carbon.csv") %>% 
  mutate(Year = strtoi(substring(Date, 7,10)))

## Rows: 5677 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (3): Country, Region, Date
## dbl (2): Kilotons of Co2, Metric Tons Per Capita
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# Summarize data by region and year
summary = data %>% 
  group_by(Region, Year) %>% 
  summarise(`Metric Tons Per Capita` = mean(`Metric Tons Per Capita`),
            .groups = 'drop')

# Create improved visualization
p <- ggplot(summary) +
  geom_line(aes(Year, `Metric Tons Per Capita`, color = Region), size = 1) +
  scale_color_brewer(palette = "Set1") +  # Better color palette for distinction
  theme_minimal() +
  labs(
    title = 'Average Metric Tons per Capita of CO2',
    subtitle = 'Separated by Region',
    x = 'Year',
    y = 'Metric Tons Per Capita',
    color = 'Region'
  ) +
  theme(
    plot.title = element_text(face = "bold", size = 14),
    axis.title = element_text(face = "bold"),
    legend.position = "bottom",
    panel.grid.minor = element_blank()
  ) +
  # Add annotations for major trend changes
  geom_point(data = subset(summary, 
                           (Region == "Europe" & Year == 2008) | 
                             (Region == "Asia" & Year == 2010)), 
             aes(Year, `Metric Tons Per Capita`, color = Region), size = 3)

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# Convert to interactive plot with plotly (if desired)

ggplotly(p, tooltip = c("Year", "Metric Tons Per Capita", "Region"))